Martin J.-D. Otis

Toward an augmented shoe for preventing falls related to physical conditions of the soil

It is known that the physical conditions of an environment might represent an important risk of falling. In this paper, we report an ongoing project toward the creation of intelligent clothes aiming at preventing falls related to such conditions. The package described here is centered on an intelligent shoe. The developed prototype counts two main parts: hardware and software. The material is composed of a set of sensors and actuators, distributed in strategic positions of the shoe, while the software is a soft real-time system running on a Smartphone. Our prototype has been served for the differentiation of physical properties of soils (concrete, broken stone, sand and dust stone).

Geometric Determination of the Interference-Free Constant-Orientation Workspace of Parallel Cable-Driven Mechanisms

The increasing use of parallel cable-driven mechanisms calls for a better understanding of their behavior and highly efficient algorithms to attenuate their drawbacks at the design stage. One of these drawbacks is the high probability of mechanical interferences between the moving parts of the mechanism. In this paper, the phenomenon is described under the assumption that a cable is a line segment in space. When a mechanical contact occurs between two cables or between a cable and an edge of the end effector, these entities necessarily lie in the same plane, and then the three-dimensional problem becomes two-dimensional. This fact is used to simplify the equations, and leads to exhaustive descriptions of the associated interference loci in the constant-orientation workspace of a cable-driven mechanism. These results provide a fast method to graphically represent all interference regions in the manipulator workspace, given its geometry and the orientation of its end effector.

Design of a serious game for learning vibrotactile messages

To prevent accidental falls, we have designed an augmented shoe aiming at assisting a user when walking. For this, the risk level (low, medium, high and very high) represented by the current situation is conveyed to the user through vibrotactile messages. In this paper, we describe the design of a serious game dedicated to learning of these signals. The game is centered on a virtual maze, whose parts are associated with the four risk levels. To explore this maze, fitted with a pair of the augmented shoes, the user is invited to walk in a room, completely empty, whose dimensions are mapped to those of the virtual maze. When moving, for each area explored the corresponding signal is delivered to the user through the augmented shoes. An initial experiment confirmed the idea that vibrotactile messages can serve for communicating the level of risk.

Towards an automatic version of the Berg balance scale test through a serious game

Considering the current pyramidal age of developed countries it appears that there is a need for tools that can automatically evaluate a persons ability to maintain balance. Proposed system, is a serious game running on the Android OS. It uses an augmented sole in order to measure movements realized by the user. For this evaluation, the same exercises proposed in the Berg Balance Scale are used; however they do receive a more motivating and engaging appeal through several events. When using the proposed system, a detailed analysis of the movements realized by the user can be done.

A serious game for training balance control over different types of soil

It is known that the type of the soil can affect balance. Here we report a serious game designed for training users at maintaining balance over five types of soil (broken stone, stone dust, sand, concrete and wood). By using an augmented shoe and proposed navigation metaphor, in this game, the user is invited to browse a maze while standing balance over the physical grounds. During the exploration, exercises targeting assessment of balance control are suggested. To insure the effectiveness of this training program, four exercises based on the Berg Balance Scale and the Tinetti Balance Assessment Tool are incorporated in the game.

A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection

It is known that head gesture and brain activity can reflect some human behaviors related to a risk of accident when using machine-tools. The research presented in this paper aims at reducing the risk of injury and thus increase worker safety. Instead of using camera, this paper presents a Smart Safety Helmet (SSH) in order to track the head gestures and the brain activity of the worker to recognize anomalous behavior. Information extracted from SSH is used for computing risk of an accident (a safety level) for preventing and reducing injuries or accidents. The SSH system is an inexpensive, non-intrusive, non-invasive, and non-vision-based system, which consists of an Inertial Measurement Unit (IMU) and dry EEG electrodes. A haptic device, such as vibrotactile motor, is integrated to the helmet in order to alert the operator when computed risk level (fatigue, high stress or error) reaches a threshold. Once the risk level of accident breaks the threshold, a signal will be sent wirelessly to stop the relevant machine tool or process.

Human Safety Algorithms for a Parallel Cable-Driven Haptic Interface

A parallel cable-driven haptic interface is designed to allow interaction with any type of virtual object. This paper presents and analyzes computational methods for addressing the issues regarding human safety and control reliability using such an interface, thereby ensuring safe operations inside the virtual world. Four strategies are explored: sensor reliability, mechanical interference management, workspace management and human-robot interaction. This paper focuses mainly on the sensors’ reliability and workspace management algorithms for a parallel cable-driven haptic interface that imposes special requirements on the control architecture design. One challenging task is to develop efficient computational algorithms for hard real-time processes included in haptic display applications which improve safety without compromising performance.

Home-Based Risk of Falling Assessment Test Using a Closed-Loop Balance Model.

The aim of this study is to improve and facilitate the methods used to assess risk of falling at home among older people through the computation of a risk of falling in real time in daily activities. In order to increase a real time computation of the risk of falling, a closed-loop balance model is proposed and compared with One-Leg Standing Test (OLST). This balance model allows studying the postural response of a person having an unpredictable perturbation. Twenty-nine volunteers participated in this study for evaluating the effectiveness of the proposed system which includes seventeen elder participants: ten healthy elderly (68.4 ±5.5 years), seven Parkinsons disease (PD) subjects (66.28 ±8.9 years), and twelve healthy young adults (28.27 ±3.74 years). Our work suggests that there is a relationship between OLST score and the risk of falling based on center of pressure measurement with four low cost force sensors located inside an instrumented insole, which could be predicted using our suggested closed-loop balance model. For long term monitoring at home, this system could be included in a medical electronic record and could be useful as a diagnostic aid tool.

Use of an enactive insole for reducing the risk of falling on different types of soil using vibrotactile cueing for the elderly

Background Our daily activities imply displacements on various types of soil. For persons with gait disorder or losing functional autonomy, walking on some types of soil could be challenging because of the risk of falling it represents. Methods In this paper, we present, in a first part, the use of an enactive shoe for an automatic differentiation of several types of soil. In a second part, using a second improved prototype (an enactive insole), twelve participants with Parkinson’s disease (PD) and nine age-matched controls have performed the Timed Up and Go (TUG) test on six types of soil with and without cueing. The frequency of the cueing was set at 10% above the cadence computed at the lower risk of falling (walking over the concrete). Depending on the cadence computed at the lower risk, the enactive insole activates a vibrotactile cueing aiming to improve gait and balance control. Finally, a risk index is computed using gait parameters in relation to given type of soil. Results The frequency analysis of the heel strike vibration allows the differentiation of various types of soil. The risk computed is associated to an appropriate rhythmic cueing in order to improve balance and gait impairment. The results show that a vibrotactile cueing could help to reduce the risk of falling. Conclusions Firstly, this paper demonstrates the feasibility of reducing the risk of falling while walking on different types of soil using vibrotactile cueing. We found a significant difference and a significant decrease in the computed risks of falling for most of types of soil especially for deformable soils which can lead to fall. Secondly, heel strike provides an approximation of the impulse response of the soil that can be analyzed with time and frequency-domain modeling. From these analyses, an index is computed enabling differentiation the types of soil.

Towards a real-time error detection within a smart home by using activity recognition with a shoe-mounted accelerometer

Abstract Taking care of the elders constitutes a major issue in the western societies. Smart homes appear to be a socially and economically viable solution. They consist in habitats augmented with sensors and actuators enabling to achieve activity recognition and to provide assistive services to a resident. Stationary aspect of sensors used in most smart homes makes the concept difficult to deploy in existing homes, and involves a high cost. In this paper, we propose an inexpensive non-vision-based system ably to recognize, in real-time, activities and errors of a resident. This proposed recognition system is based on a shoe equipped with a single sensor: a three-axis accelerometer and on a state- transition algorithmic approach using fuzzy logic. We have examined the learning data as frequency distributions, where the probability histograms have been directly interpreted as fuzzy set. We conducted experiments of the system in our smart home by simulating (multiple times) several scenarios based on a morning routine. These scenarios were based on clinical data gathered in a previous experiment with actual Alzheimers patients. We obtained promising results showing that the proposed activity and error recognition system are highly effective.